Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network
Advancements in molecular separations coupled with mass spectrometry have enabled large-scale metabolome analyses for clinical cohorts. A population of interest for metabolome profiling are patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic bases, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Un-diagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN pa-tients took place over three years. We describe the study design to adjust for measurements collected over a long time-scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case-studies of interest that demonstrate the utility of metabolome profiling for rare diseases.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1843017
- Report Number(s):
- PNNL-SA--145014
- Journal Information:
- Analytical Chemistry, Journal Name: Analytical Chemistry Journal Issue: 2 Vol. 92; ISSN 0003-2700
- Publisher:
- American Chemical Society (ACS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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